Vincent Chin-Hung Chen,1,2 Yi-Chun Liu,3 Seh-Huang Chao,4 Roger S McIntyre,5–7 Danielle S Cha,5,8 Yena Lee,5,6 Jun-Cheng Weng2,9
1School of Medicine, Chang Gung University, Taoyuan, Taiwan; 2Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan; 3Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan; 4Center of Metabolic and Bariatric Surgery, Jen-Ai Hospital, Taichung, Taiwan; 5Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada; 6Institute of Medical Science, University of Toronto, Toronto, ON, Canada; 7Departments of Psychiatry and Pharmacology, University of Toronto, Toronto, ON, Canada; 8School of Medicine, University of Queensland, Queensland, Brisbane, Australia; 9Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
Purpose: Obesity is a complex and multifactorial disease identified as a global epidemic. Convergent evidence indicates that obesity differentially influences patients with neuropsychiatric disorders providing a basis for hypothesizing that obesity alters brain structure and function associated with the brain’s propensity toward disturbances in mood and cognition. Herein, we characterize alterations in brain structures and networks among obese subjects (ie, body mass index [BMI] ≥30 kg/m2) when compared with non-obese controls.
Patients and methods: We obtained noninvasive diffusion tensor imaging and generalized q-sampling imaging scans of 20 obese subjects (BMI=37.9±5.2 SD) and 30 non-obese controls (BMI=22.6±3.4 SD). Graph theoretical analysis and network-based statistical analysis were performed to assess structural and functional differences between groups. We additionally assessed for correlations between diffusion indices, BMI, and anxiety and depressive symptom severity (ie, Hospital Anxiety and Depression Scale total score).
Results: The diffusion indices of the posterior limb of the internal capsule, corona radiata, and superior longitudinal fasciculus were significantly lower among obese subjects when compared with controls. Moreover, obese subjects were more likely to report anxiety and depressive symptoms. There were fewer structural network connections observed in obese subjects compared with non-obese controls. Topological measures of clustering coefficient (C), local efficiency (Elocal), global efficiency (Eglobal), and transitivity were significantly lower among obese subjects. Similarly, three sub-networks were identified to have decreased structural connectivity among frontal–temporal regions in obese subjects compared with non-obese controls.
Conclusion: We extend knowledge further by delineating structural interconnectivity alterations within and across brain regions that are adversely affected in individuals who are obese.
Keywords: obesity, diffusion tensor imaging, DTI, generalized q-sampling imaging, GQI, graph theoretical analysis, GTA, network-based statistical analysis, NBS
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